Implementation Details. The input image resolution of our framework is 512 \(\times \)  512. The image data augmentation is flipped horizontally or vertically with a probability of 0.5. We use the ResNet-50 [1]} as our backbone network and the weight is initialized with ImageNet [2]} pretrained model. All experiments are trained in 4 2080Ti GPUs and batch size is set to 8. The stochastic gradient descent (SGD) solver is adopted in 50K iterations. The momentum is set to 0.9 and weight decay is set to 0.0005. The learning rate is initially set to 0.01 and decreases linearly.
<TABLE>